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LlamaIndex — .klickd v4.0.0 Integration

Framework: LlamaIndex · pip install llama-index SDK: pip install klickd==4.0.0

LlamaIndex offers two natural integration points for .klickd:

  1. System-prompt injection — same pattern as LangChain / OpenAI; the payload becomes the system message of a chat engine or agent.
  2. Document ingestion — the structured fields of a .klickd payload are turned into Document objects and embedded into a vector index alongside the rest of the user's corpus.

The reusable helpers live at examples/v4/integrations/llamaindex/klickd_llamaindex.py.

1. System-prompt injection

from klickd import load_klickd
from llama_index.llms.openai import OpenAI
from llama_index.core.llms import ChatMessage

from examples.v4.integrations.llamaindex.klickd_llamaindex import klickd_to_system_prompt

with open("examples/v4/personas/01-eleve-terminale-fr.klickd", "rb") as f:
    payload = load_klickd(f.read())

system_prompt = klickd_to_system_prompt(payload)

llm = OpenAI(model="gpt-4o")
response = llm.chat([
    ChatMessage(role="system", content=system_prompt),
    ChatMessage(role="user", content="Let's continue."),
])
print(response.message.content)

2. Document ingestion (vector index)

from klickd import load_klickd
from llama_index.core import VectorStoreIndex

from examples.v4.integrations.llamaindex.klickd_llamaindex import klickd_to_documents

with open("examples/v4/personas/03-fullstack-developer-en.klickd", "rb") as f:
    payload = load_klickd(f.read())

docs = klickd_to_documents(payload)
index = VectorStoreIndex.from_documents(docs)

query_engine = index.as_query_engine()
print(query_engine.query("What is the user currently blocked on?"))

klickd_to_documents() emits one Document per structurally distinct section (user_preferences, context, knowledge, plus one per memory[] entry), with metadata carrying the section name, version, and the original domain / profile_kind so you can filter on retrieval.

Notes

  • Strip _-prefixed debug / benchmark fields before ingest — the helper does this for you.
  • Encrypted envelopes: pass passphrase=... to load_klickd_path().
  • Treat the index as user-scoped: never co-mingle multiple users' .klickd payloads in the same vector store without a tenant filter.